code: def ConvLayer(
      in_channels,
      out_channels,
      kernel_size=3,
      stride=1,
      bias_attr=False,
  ):
      layer = nn.Sequential(
          (
              "conv",
              nn.Conv2D(
                  in_channels,
                  out_channels,
                  kernel_size=kernel_size,
                  stride=stride,
                  padding=kernel_size // 2,
                  groups=1,
                  bias_attr=bias_attr,
              ),
          ),
          ("norm", nn.BatchNorm2D(out_channels)),
          ("relu", nn.ReLU6()),
      )
      return layer
  
file_path: PaddleDetection/ppdet/modeling/backbones/hardnet.py
imports: import paddle
  import paddle.nn
  from ppdet.core.workspace import register
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 1 for calling a global function with example usage: ConvLayer(in_channels, out_channels, kernel_size, stride, bias_attr) and returns: layer